Abstract

It is increasingly likely that the high incidence of off-target effects associated with targeted inhibitors is due, in part, to the highly complex and dysregulated intracellular molecular networks associated with cancer. Ignoring this complexity can lead to suboptimal results and subsequent loss of life through ineffective therapies. The phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) pathways are two of the most dysregulated pathways across all cancers. Several regulatory mechanisms have been proposed to explain the apparent cross-talk between them. For example, RAS to ERK signaling in the MAPK pathway has been proposed as an important metastases “escape mechanism” when PI3K is inhibited. In order to rationally develop precise therapeutic avenues to target these oncogenic pathways, it is critical to understand how the pathways are wired as an integrated network, in both normal and tumor cells.

We have developed a systems biology approach that integrates measurements of protein activation under diverse experimental conditions, including inhibition of MEK and PI3K, with a novel network inference computational model that predicts the causal connectivity of a network from experimental data. The network inference model utilizes a genetic algorithm to search for the “optimal” network configuration that most closely matches the experimental data used as input. We validated the approach using in silico data generated from a set of randomized test networks. Next, we applied this approach to breast epithelial cell lines (MFC10A, MCF7, MDA-MB-231, and SUM149) by performing a set of experiments using a series of pathway specific inhibitors with and without growth factor stimulation. From phospho western blot readouts of several proteins in the PI3K and MAPK pathways, we have predicted network configurations most likely responsible for the distinct experimental output of each of the four cell lines. In some cases, our network inference model predicted multiple optimal networks for a given cell line. Our method predicted the next experiments needed to optimally distinguish between the set of possible candidate networks. Our results suggest that some proposed interactions and feedback mechanisms attributed to MAPK and PI3K cross-talk in the literature may not be valid.

This approach has important implications for the prospect of effective personalized cancer treatments and targeted molecular inhibition. Elucidating the mechanisms of cross-talk between the MAPK and PI3K pathways in cells collected from patient tumors will permit rational discovery of the optimal combination of targeted therapies needed to treat a specific cancer, based on the actual network that is operational in the tumor. Moreover, our methodology and predictive network model can be applied to any set of signaling pathways.